As the first to focus on the issue of Data Warehouse Requirements Engineering, this book introduces a model-driven requirements process used to identify requirements granules and incrementally develop data warehouse fragments. In addition, it presents an approach to the pair-wise integration of requirements granules for consolidating multiple data warehouse fragments. The process is systematic and does away with the fuzziness associated with existing techniques. Thus, consolidation is treated as a requirements engineering issue. The notion of a decision occupies a central position in the decision-based approach. On one hand, information relevant to a decision must be elicited from stakeholders; modeled; and transformed into multi-dimensional form. On the other, decisions themselves are to be obtained from decision applications. For the former, the authors introduce a suite of information elicitation techniques specific to data warehousing. This information is subsequently converted into multi-dimensional form. For the latter, not only are decisions obtained from decision applications for managing operational businesses, but also from applications for formulating business policies and for defining rules for enforcing policies, respectively. In this context, the book presents a broad range of models, tools and techniques. For readers from academia, the book identifies the scientific/technological problems it addresses and provides cogent arguments for the proposed solutions; for readers from industry, it presents an approach for ensuring that the product meets its requirements while ensuring low lead times in delivery.
Data warehousing and online analysis technologies have shown their effectiveness in managing and analyzing a large amount of disparate data, attracting much attention from numerous research communities. Data Warehousing Design and Advanced Engineering Applications: Methods for Complex Construction covers the complete process of analyzing data to extract, transform, load, and manage the essential components of a data warehousing system. A defining collection of field discoveries, this advanced title provides significant industry solutions for those involved in this distinct research community.
Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work illustrates the agile interpretations of the remaining software engineering disciplines: Requirements management benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked. Data engineering receives two new "hyper modeling" techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs. Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines. Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way. Learn how to quickly define scope and architecture before programming starts Includes techniques of process and data engineering that enable iterative and incremental delivery Demonstrates how to plan and execute quality assurance plans and includes a guide to continuous integration and automated regression testing Presents program management strategies for coordinating multiple agile data mart projects so that over time an enterprise data warehouse emerges Use the provided 120-day road map to establish a robust, agile data warehousing program
This book constitutes the refereed proceedings of the First International Conference on Smart Trends in Information Technology and Computer Communications, SmartCom 2016, held in Jaipur, India, in August 2016. The 106 revised papers presented were carefully reviewed and selected from 469 submissions. The papers address issues on smart and secure systems; technologies for digital world; data centric approaches; applications for e-agriculture and e-health; products and IT innovations; research for knowledge computing.
This book constitutes the post-conference proceedings of the CAiSE Forum from the 22nd International Conference on Advanced Information Systems Engineering (CAiSE 2010), held in Hammamet, Tunisia, June 9, 2010. While the CAiSE conference itself focuses on papers that report on matured research, the CAiSE forum was created specifically as a platform to present fresh ideas, new concepts, and new and innovative systems, tools, and applications. The 22 papers presented in this volume were carefully reviewed and selected from 32 submissions. The reworked and extended versions of the original presentations cover topics such as business process management, enterprise architecture and modeling, service-oriented architectures, and requirements engineering.
This book constitutes the refereed proceedings of workshops, held at the 32nd International Conference on Conceptual Modeling, ER 2013, in Hong Kong, China in November 2013. The 30 revised full papers were carefully reviewed and selected out of 57 submissions. The papers are organized in sections related to the individual workshops: LSAWM, Legal and Social Aspects in Web Modeling; MoBiD, 1st International Workshop on Modeling and Management of Big Data; RIGiM, 5th International Workshop on Requirements, Intentions and Goals in Conceptual Modeling; SeCoGIS, 7th International Workshop on Semantic and Conceptual Issues in Geographic Information Systems; WISM, 10th International Workshop on Web Information Systems Modeling; DaSeM, Data Mining and Semantic Computing for Object Modeling; SCME, 1st Symposium on Conceptual Modeling Education; and PhD Symposium. Continuing the ER tradition, the ER 2013 workshops provided researchers, students, and industry professionals with a forum to present and discuss emerging, cutting-edge topics related to conceptual modeling and its applications.
Data warehousing is one of the hottest topics in the computing industry. Written by Barry Devlin, one of the world's leading experts on data warehousing, this book gives you the insights and experiences gained over 10 years and offers the most comprehensive, practical guide to designing, building, and implementing a successful data warehouse. Included in this vital information is an explanation of the optimal three-tiered architecture for the data warehouse, with a clear division between data and information. Information systems managers will appreciate the full description of the functions needed to implement such an architecture, including reconciling existing, diverse data and deriving consistent, valuable business information.
Geared to IT professionals eager to get into the all-importantfield of data warehousing, this book explores all topics needed bythose who design and implement data warehouses. Readers will learnabout planning requirements, architecture, infrastructure, datapreparation, information delivery, implementation, and maintenance.They'll also find a wealth of industry examples garnered from theauthor's 25 years of experience in designing and implementingdatabases and data warehouse applications for majorcorporations. Market: IT Professionals, Consultants.
The quality of a data warehouse (DWH) is the elusive aspect of it, not because it is hard to achieve [once we agree what it is], but because it is difficult to describe. We propose the notion that quality is not an attribute or a feature that a product has to possess, but rather a relationship between that product and each and every stakeholder. More specifically, the relationship between the software quality and the organization that produces the products is explored. Quality of data that populates the DWH is the main concern of the book, therefore we propose a definition for data quality as: "fitness to serve each and every purpose". Methods are proposed throughout the book to help readers achieve data warehouse quality.
This exceptional work provides readers with an introduction to the state-of-the-art research on data warehouse design, with many references to more detailed sources. It offers a clear and a concise presentation of the major concepts and results in the subject area. Malinowski and Zimányi explain conventional data warehouse design in detail, and additionally address two innovative domains recently introduced to extend the capabilities of data warehouse systems: namely, the management of spatial and temporal information.
The Third Edition of this well-received text analyses the fundamental concepts of data warehousing, data marts, and OLAP. The author discusses, in an easy-to-understand language, important topics such as data mining, how to build a data warehouse, and potential applications of data warehousing technology in government. Besides, the text compares and contrasts the currently available software tools used to design and develop data warehouses. While retaining the six existing case studies, it gives four new case studies: HARBOR, A Highly Available Data Warehouse A Typical Business Data Warehouse for a Trading Company Customer Data Warehouse for the World’s First and Largest Online Bank in the United Kingdom A German Supermarket EDEKA’s Data Warehouse The book, which is a blend of principles and real-life case studies, is intended as a text for students of B.Tech/M.Tech (Computer Science and Engineering), B.Tech/M.Tech (Information Technology), MBA, M.Sc. (Computer Science), M.Sc. (Information Technology), and MCA. It should also be of considerable utility and worth to software professionals and database practitioners.
In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.
Data Warehousing for Biomedical Informatics is a step-by-step how-to guide for designing and building an enterprise-wide data warehouse across a biomedical or healthcare institution, using a four-iteration lifecycle and standardized design pattern. It enables you to quickly implement a fully-scalable generic data architecture that supports your org
Data warehouses and online analytical processing (OLAP) are emerging key technologies for enterprise decision support systems. They provide sophisticated technologies from data integration, data collection and retrieval, query optimization, and data analysis to advanced user interfaces. New research and technological achievements in the area of data warehousing are implemented in commercial database management systems, and organizations are developing data warehouse systems into their information system infrastructures. Data Warehouses and OLAP: Concepts, Architectures and Solutions covers a wide range of technical, technological, and research issues. It provides theoretical frameworks, presents challenges and their possible solutions, and examines the latest empirical research findings in the area. It is a resource of possible solutions and technologies that can be applied when designing, implementing, and deploying a data warehouse, and assists in the dissemination of knowledge in this field.
As it is with building a house, most of the work necessary to build a data warehouse is neither visible nor obvious when looking at the completed product. While it may be easy to plan for a data warehouse that incorporates all the right concepts, taking the steps needed to create a warehouse that is as functional and user-friendly as it is theoreti